Abstract
Language  Undefined 

Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  16 Oct 2015 
Place of Publication  Enschede, The Netherlands 
Publisher  
Print ISBNs  9789036539326 
DOIs  
Publication status  Published  16 Oct 2015 
Keywords
 EWI26925
 IR97338
 METIS311922
Cite this
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Cooccurrence rate networks: towards separate training for undirected graphical models. / Zhu, Zhemin.
Enschede, The Netherlands : Universiteit Twente, 2015. 178 p.Research output: Thesis › PhD Thesis  Research UT, graduation UT › Academic
TY  THES
T1  Cooccurrence rate networks: towards separate training for undirected graphical models
AU  Zhu, Zhemin
N1  SIKS Dissertation Series No. 201522
PY  2015/10/16
Y1  2015/10/16
N2  Dependence is a universal phenomenon which can be observed everywhere. In machine learning, probabilistic graphical models (PGMs) represent dependence relations with graphs. PGMs find wide applications in natural language processing (NLP), speech processing, computer vision, biomedicine, information retrieval, etc. Many traditional models, such as hidden Markov models (HMMs), Kalman filters, can be put under the umbrella of PGMs. The central idea of PGMs is to decompose (factorize) a joint probability into a product of local factors. Learning, inference and storage can be conducted efficiently over the factorization representation. In this thesis, we propose a novel framework motivated by the Minimum Shared Information Principle (MSIP): We try to find a factorization in which the information shared between factors is minimum. In other words, we try to make factors as independent as possible. The benefit by doing this is that we can train factors separately without paying a lot of efforts to guarantee consistency between them. To achieve this goal, we develop a theoretical framework called cooccurrence rate networks (CRNs) to obtain such a factorization. Experimental results on three important natural language processing tasks show that our separate training method is two orders of magnitude faster than conditional random fields, while achieving competitive quality (often better on the overall quality metric F1). The second contribution of this thesis is applying PGMs to a realworld NLP application: open relation extraction (ORE). In open relation extraction, two entities in a sentence are given, and the goal is to automatically extract their relation expression. ORE is a core technique, especially in the age of big data, for transforming unstructured information into structured data. We propose our model SimpleIE for this task. The basic idea is to decompose an extraction pattern into a sequence of simplification operations (components). The benefit by doing this is that these components can be recombined in a new way to generate new extraction patterns. Experimental results on three benchmark data sets show that SimpleIE boosts recall and F1 by at least 15% comparing with seven ORE systems.
AB  Dependence is a universal phenomenon which can be observed everywhere. In machine learning, probabilistic graphical models (PGMs) represent dependence relations with graphs. PGMs find wide applications in natural language processing (NLP), speech processing, computer vision, biomedicine, information retrieval, etc. Many traditional models, such as hidden Markov models (HMMs), Kalman filters, can be put under the umbrella of PGMs. The central idea of PGMs is to decompose (factorize) a joint probability into a product of local factors. Learning, inference and storage can be conducted efficiently over the factorization representation. In this thesis, we propose a novel framework motivated by the Minimum Shared Information Principle (MSIP): We try to find a factorization in which the information shared between factors is minimum. In other words, we try to make factors as independent as possible. The benefit by doing this is that we can train factors separately without paying a lot of efforts to guarantee consistency between them. To achieve this goal, we develop a theoretical framework called cooccurrence rate networks (CRNs) to obtain such a factorization. Experimental results on three important natural language processing tasks show that our separate training method is two orders of magnitude faster than conditional random fields, while achieving competitive quality (often better on the overall quality metric F1). The second contribution of this thesis is applying PGMs to a realworld NLP application: open relation extraction (ORE). In open relation extraction, two entities in a sentence are given, and the goal is to automatically extract their relation expression. ORE is a core technique, especially in the age of big data, for transforming unstructured information into structured data. We propose our model SimpleIE for this task. The basic idea is to decompose an extraction pattern into a sequence of simplification operations (components). The benefit by doing this is that these components can be recombined in a new way to generate new extraction patterns. Experimental results on three benchmark data sets show that SimpleIE boosts recall and F1 by at least 15% comparing with seven ORE systems.
KW  EWI26925
KW  IR97338
KW  METIS311922
U2  10.3990/1.9789036539326
DO  10.3990/1.9789036539326
M3  PhD Thesis  Research UT, graduation UT
SN  9789036539326
PB  Universiteit Twente
CY  Enschede, The Netherlands
ER 